Smart Charging Scheduling Management for Suitable Allocation of EV Under Uncertain Spatiotemporal Environment

In the Anthropocene era, the scarcity of electric vehicle (EV) charging stations has heightened concerns among EV owners, often resulting in breakdowns due to inadequate station selection. They are often confused “when” & “where” to charge the EV. Existing literature reveals that EVs are typically assigned to the nearest charging station without considering traffic flow, leading to inefficiencies. Additionally, checking slot availability is crucial. Simultaneously, multiple vehicles may converge on a station, overwhelming limited charging ports and causing chaos. Consequently, this not only disrupts charging station operations but also strains electrical utilities. To address these challenges, this paper introduces a novel charging scheduling management technique. Leveraging integer linear programming (ILP) and queuing theory optimization (QTO), our approach intelligently allocates EVs to appropriate charging stations, minimizing both battery energy consumption and wait times. Furthermore, sensitivity analysis validates the algorithm’s robustness. Through this framework, it is aimed to enhance the efficiency and reliability of EV charging infrastructure in a dynamically changing environment.